Symbiotic Organisms Search Optimization to Predict Optimal Thread Count for Multi-threaded Applications

Multicore systems have emerged as a cost-effective option for the growing demands for high-performance, low-energy computing. Thread management has long been a source of concern for developers, as overheads associated with it reduce the overall throughput of the multicore processor systems. One of the most complex problems with multicore processors is determining the optimal number of threads for the execution of multithreaded programs. To address this issue, this paper proposes a novel solution based on a modified symbiotic organism search (MSOS) algorithm which is a bio-inspired algorithm used for optimization in various engineering domains. This technique uses mutualism, commensalism and parasitism behaviours seen in organisms for searching the optimal solutions in the available search space. The algorithm is simulated on the NVIDIA DGX Intel-Xeon E5-2698-v4 server with PARSEC 3.0 benchmark suit.  The results show that keeping the thread count equal to the number of processors available in the system is not necessarily the best strategy to get maximum speedup when running multithreaded programs. It was also observed that when programs are run with the optimal thread count, the execution time is substantially decreased, resulting in energy savings due to the use of fewer processors than are available in the system

A mathematical programming approach for resource allocation of data analysis workflows on heterogeneous clusters

Scientific communities are motivated to schedule their large-scale data analysis workflows in heterogeneous cluster environments because of privacy and financial issues. In such environments containing considerably diverse resources, efficient resource allocation approaches are essential for reaching high performance. Accordingly, this research addresses the scheduling problem of workflows with bag-of-task form to minimize total runtime (makespan). To this aim, we develop a mixed-integer linear programming model (MILP). The proposed model contains binary decision variables determining which tasks should be assigned to which nodes. Also, it contains linear constraints to fulfill the tasks requirements such as memory and scheduling policy. Comparative results show that our approach outperforms related approaches in most cases. As part of the post-optimality analysis, some secondary preferences are imposed on the proposed model to obtain the most preferred optimal solution. We analyze the relaxation of the makespan in the hope of significantly reducing the number of consumed nodes

Distributed computational model for shared processing on Cyber-Physical System environments

Cyber-Physical Systems typically consist of a combination of mobile devices, embedded systems and computers to monitor, sense, and actuate with the surrounding real world. These computing elements are usually wireless, interconnected to share data and interact with each other, with the server part and also with cloud computing services. In such a heterogeneous environment, new applications arise to meet ever-increasing needs and these are an important challenge to the processing capabilities of devices. For example, automatic driving systems, manufacturing environments, smart city management, etc. To meet the requirements of said application contexts, the system can create computing processes to distribute the workload over the network and/or a cloud computing server. Multiple options arise in relation to what network nodes should support the execution of the processes. This paper focuses on this problem by introducing a distributed computational model to dynamically share these tasks among the computing nodes and considering the inherent variability of the context in these environments. Our novel approach promotes the integration of the computing resources, with externally supplied cloud services, to fulfill modern application requirements. A prototype implementation for the proposed model has been built and an application example has been designed to validate the proposal in a real working environment